Design and implementation of a scalable microservice-based cloud computing service for stochastic fluid flow modeling

This paper presents the design and performance evaluation of a scalable microservice-based cloud computing framework for distributed stochastic fluid flow simulations. The system integrates Docker containerization, NGINX load balancing, and Apache Kafka for asynchronous task coordination and efficient workload distribution across computational nodes. The backend combines PHP and Julia for orchestration and computation, with PostgreSQL managing task and result data. Benchmark experiments demonstrate near-linear scalability and stable performance under varying loads, confirming the system’s suitability for high-performance scientific computing. The proposed framework advances parallel and distributed computing by introducing a modular, reproducible architecture for executing complex stochastic models in cloud environments. • Introduces a next-generation microservice framework for distributed scientific computing. • Unifies Docker, NGINX, and Kafka to achieve seamless parallel orchestration and scalability. • Delivers near-linear performance gains across multi-node cloud infrastructures. • Ensures fault-tolerant, event-driven execution for complex stochastic simulations. • Promotes reproducible, modular, and openly deployable cloud-based research workflows.